Hurricane Forecasting: A Novel Multimodal Machine Learning Framework

TL;DR

This paper introduces Hurricast, a multimodal machine learning framework that combines diverse data sources and advanced ML techniques to improve tropical cyclone forecasting accuracy and speed, with potential to enhance official forecasts.

Contribution

The paper presents a novel multimodal ML framework, Hurricast, integrating deep learning and gradient-boosted trees for improved cyclone track and intensity prediction.

Findings

Achieves comparable accuracy to current operational models.

Computes forecasts in seconds, enabling rapid predictions.

Potential to improve official hurricane forecast consensus.

Abstract

This paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting features with deep-learning encoder-decoder architectures and predicting with gradient-boosted trees. We evaluate our models in the North Atlantic and Eastern Pacific basins on 2016-2019 for 24-hour lead time track and intensity forecasts and show they achieve comparable mean absolute error and skill to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an operational forecast consensus model could improve over the National Hurricane Center's official forecast, thus highlighting the complementary properties with existing approaches. In summary, our work demonstrates that utilizing machine learning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.